\section{Learning Methods}
\label{sec:methods}

\subsection{Bayesian Classification}
The first model that we train uses a multinomial model of Naive Bayes Learner. 
We use the Weka implementation of the same in Rapidminer.
After preprocessing all of the data as stated Section \ref{sec:preprocess}, we feed our data to the
Naive Bayes Learner operator. This implementation of Weka has a fixed pseudocount of $1$. 

\subsection{Support Vector Machine}
The second model we train uses a Fast Large Margin operator with L2 SVM Primal. 
We use a grid search to find a suitable strength of regularization $C$. Since the number of
examples is small in comparison to the number of parameters, we expect to need
a large strength of regularization. Since $C$ is inversely proportional to the
strength of regularization, we expect a small value of $C$. We run our grid
search on values $10^c$ where $c$ ranges from $-4$ to $4$ in increments of 1.


% Sample equation
\ignore{
\begin{equation}
\label{eq:e_abs}
e(f(r_i, c_j), x_{ij}) = \mu(r_i^2 + c_j^2) + |r_ic_j - x_{ij}|
\end{equation}
}

